Overview

Dataset statistics

Number of variables8
Number of observations569
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.7 KiB
Average record size in memory64.2 B

Variable types

Categorical1
Numeric7

Alerts

concave points_mean is highly overall correlated with radius_worst and 4 other fieldsHigh correlation
radius_worst is highly overall correlated with concave points_mean and 4 other fieldsHigh correlation
perimeter_worst is highly overall correlated with concave points_mean and 4 other fieldsHigh correlation
concave points_worst is highly overall correlated with concave points_mean and 5 other fieldsHigh correlation
area_worst is highly overall correlated with concave points_mean and 4 other fieldsHigh correlation
smoothness_worst is highly overall correlated with concave points_worstHigh correlation
diagnosis is highly overall correlated with concave points_mean and 4 other fieldsHigh correlation
concave points_mean has 13 (2.3%) zerosZeros
concave points_worst has 13 (2.3%) zerosZeros

Reproduction

Analysis started2023-07-19 17:30:31.070427
Analysis finished2023-07-19 17:30:52.236386
Duration21.17 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

diagnosis
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
0
357 
1
212 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters569
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 357
62.7%
1 212
37.3%

Length

2023-07-19T12:30:52.641114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T12:30:53.157355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 357
62.7%
1 212
37.3%

Most occurring characters

ValueCountFrequency (%)
0 357
62.7%
1 212
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 569
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 357
62.7%
1 212
37.3%

Most occurring scripts

ValueCountFrequency (%)
Common 569
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 357
62.7%
1 212
37.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 357
62.7%
1 212
37.3%

concave points_mean
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct542
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.048919146
Minimum0
Maximum0.2012
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-07-19T12:30:53.434012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0056208
Q10.02031
median0.0335
Q30.074
95-th percentile0.12574
Maximum0.2012
Range0.2012
Interquartile range (IQR)0.05369

Descriptive statistics

Standard deviation0.038802845
Coefficient of variation (CV)0.79320365
Kurtosis1.0665557
Mean0.048919146
Median Absolute Deviation (MAD)0.02014
Skewness1.1711801
Sum27.834994
Variance0.0015056608
MonotonicityNot monotonic
2023-07-19T12:30:53.955423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
2.3%
0.02864 3
 
0.5%
0.1471 2
 
0.4%
0.05778 2
 
0.4%
0.02272 2
 
0.4%
0.02369 2
 
0.4%
0.02377 2
 
0.4%
0.02594 2
 
0.4%
0.05252 2
 
0.4%
0.02031 2
 
0.4%
Other values (532) 537
94.4%
ValueCountFrequency (%)
0 13
2.3%
0.001852 1
 
0.2%
0.002404 1
 
0.2%
0.002924 1
 
0.2%
0.002941 1
 
0.2%
0.003125 1
 
0.2%
0.003261 1
 
0.2%
0.003333 1
 
0.2%
0.003472 1
 
0.2%
0.004167 1
 
0.2%
ValueCountFrequency (%)
0.2012 1
0.2%
0.1913 1
0.2%
0.1878 1
0.2%
0.1845 1
0.2%
0.1823 1
0.2%
0.1689 1
0.2%
0.162 1
0.2%
0.1604 1
0.2%
0.1595 1
0.2%
0.1562 1
0.2%

radius_worst
Real number (ℝ)

HIGH CORRELATION 

Distinct457
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.26919
Minimum7.93
Maximum36.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-07-19T12:30:54.329093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.93
5-th percentile10.534
Q113.01
median14.97
Q318.79
95-th percentile25.64
Maximum36.04
Range28.11
Interquartile range (IQR)5.78

Descriptive statistics

Standard deviation4.8332416
Coefficient of variation (CV)0.29707943
Kurtosis0.94408958
Mean16.26919
Median Absolute Deviation (MAD)2.46
Skewness1.1031152
Sum9257.169
Variance23.360224
MonotonicityNot monotonic
2023-07-19T12:30:54.628272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.36 5
 
0.9%
13.34 4
 
0.7%
13.5 4
 
0.7%
12.84 3
 
0.5%
15.14 3
 
0.5%
13.75 3
 
0.5%
13.35 3
 
0.5%
15.53 3
 
0.5%
16.76 3
 
0.5%
19.85 3
 
0.5%
Other values (447) 535
94.0%
ValueCountFrequency (%)
7.93 1
0.2%
8.678 1
0.2%
8.952 1
0.2%
8.964 1
0.2%
9.077 1
0.2%
9.092 1
0.2%
9.262 1
0.2%
9.414 1
0.2%
9.456 1
0.2%
9.473 1
0.2%
ValueCountFrequency (%)
36.04 1
0.2%
33.13 1
0.2%
33.12 1
0.2%
32.49 1
0.2%
31.01 1
0.2%
30.79 1
0.2%
30.75 1
0.2%
30.67 1
0.2%
30 1
0.2%
29.92 1
0.2%

perimeter_worst
Real number (ℝ)

HIGH CORRELATION 

Distinct514
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.26121
Minimum50.41
Maximum251.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-07-19T12:30:54.951228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.41
5-th percentile67.856
Q184.11
median97.66
Q3125.4
95-th percentile171.64
Maximum251.2
Range200.79
Interquartile range (IQR)41.29

Descriptive statistics

Standard deviation33.602542
Coefficient of variation (CV)0.31327767
Kurtosis1.0701497
Mean107.26121
Median Absolute Deviation (MAD)16.87
Skewness1.1281639
Sum61031.63
Variance1129.1308
MonotonicityNot monotonic
2023-07-19T12:30:55.284997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.7 3
 
0.5%
105.9 3
 
0.5%
101.7 3
 
0.5%
184.6 2
 
0.4%
106.4 2
 
0.4%
79.93 2
 
0.4%
92.04 2
 
0.4%
145.4 2
 
0.4%
127.1 2
 
0.4%
89 2
 
0.4%
Other values (504) 546
96.0%
ValueCountFrequency (%)
50.41 1
0.2%
54.49 1
0.2%
56.65 1
0.2%
57.17 1
0.2%
57.26 1
0.2%
58.08 1
0.2%
58.36 1
0.2%
59.16 1
0.2%
59.9 1
0.2%
60.9 1
0.2%
ValueCountFrequency (%)
251.2 1
0.2%
229.3 1
0.2%
220.8 1
0.2%
214 1
0.2%
211.7 1
0.2%
211.5 1
0.2%
206.8 1
0.2%
206 1
0.2%
205.7 1
0.2%
202.4 1
0.2%

concave points_worst
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct492
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11460622
Minimum0
Maximum0.291
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-07-19T12:30:55.726307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.024286
Q10.06493
median0.09993
Q30.1614
95-th percentile0.23692
Maximum0.291
Range0.291
Interquartile range (IQR)0.09647

Descriptive statistics

Standard deviation0.065732341
Coefficient of variation (CV)0.57354949
Kurtosis-0.53553512
Mean0.11460622
Median Absolute Deviation (MAD)0.04457
Skewness0.49261553
Sum65.210941
Variance0.0043207407
MonotonicityNot monotonic
2023-07-19T12:30:56.078524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
2.3%
0.05556 3
 
0.5%
0.06296 3
 
0.5%
0.1218 3
 
0.5%
0.07431 3
 
0.5%
0.1708 3
 
0.5%
0.1105 3
 
0.5%
0.02564 3
 
0.5%
0.04306 3
 
0.5%
0.1827 3
 
0.5%
Other values (482) 529
93.0%
ValueCountFrequency (%)
0 13
2.3%
0.008772 1
 
0.2%
0.009259 1
 
0.2%
0.01042 1
 
0.2%
0.01111 2
 
0.4%
0.01389 1
 
0.2%
0.01635 1
 
0.2%
0.01667 1
 
0.2%
0.01852 1
 
0.2%
0.02022 1
 
0.2%
ValueCountFrequency (%)
0.291 1
0.2%
0.2903 1
0.2%
0.2867 1
0.2%
0.2756 1
0.2%
0.2733 1
0.2%
0.2701 1
0.2%
0.2688 1
0.2%
0.2685 1
0.2%
0.2654 1
0.2%
0.265 1
0.2%

texture_mean
Real number (ℝ)

Distinct479
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.289649
Minimum9.71
Maximum39.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-07-19T12:30:56.411890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.71
5-th percentile13.088
Q116.17
median18.84
Q321.8
95-th percentile27.15
Maximum39.28
Range29.57
Interquartile range (IQR)5.63

Descriptive statistics

Standard deviation4.3010358
Coefficient of variation (CV)0.22297118
Kurtosis0.75831897
Mean19.289649
Median Absolute Deviation (MAD)2.81
Skewness0.65044954
Sum10975.81
Variance18.498909
MonotonicityNot monotonic
2023-07-19T12:30:56.730742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.52 3
 
0.5%
16.85 3
 
0.5%
16.84 3
 
0.5%
19.83 3
 
0.5%
14.93 3
 
0.5%
17.46 3
 
0.5%
18.9 3
 
0.5%
15.7 3
 
0.5%
18.22 3
 
0.5%
20.22 2
 
0.4%
Other values (469) 540
94.9%
ValueCountFrequency (%)
9.71 1
0.2%
10.38 1
0.2%
10.72 1
0.2%
10.82 1
0.2%
10.89 1
0.2%
10.91 1
0.2%
10.94 1
0.2%
11.28 1
0.2%
11.79 1
0.2%
11.89 1
0.2%
ValueCountFrequency (%)
39.28 1
0.2%
33.81 1
0.2%
33.56 1
0.2%
32.47 1
0.2%
31.12 1
0.2%
30.72 1
0.2%
30.62 1
0.2%
29.97 1
0.2%
29.81 1
0.2%
29.43 1
0.2%

area_worst
Real number (ℝ)

HIGH CORRELATION 

Distinct544
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean880.58313
Minimum185.2
Maximum4254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-07-19T12:30:57.166774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum185.2
5-th percentile331.06
Q1515.3
median686.5
Q31084
95-th percentile2009.6
Maximum4254
Range4068.8
Interquartile range (IQR)568.7

Descriptive statistics

Standard deviation569.35699
Coefficient of variation (CV)0.64656814
Kurtosis4.3963948
Mean880.58313
Median Absolute Deviation (MAD)215.6
Skewness1.8593733
Sum501051.8
Variance324167.39
MonotonicityNot monotonic
2023-07-19T12:30:57.482368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
472.4 2
 
0.4%
1210 2
 
0.4%
826.4 2
 
0.4%
402.8 2
 
0.4%
1750 2
 
0.4%
706 2
 
0.4%
830.5 2
 
0.4%
546.7 2
 
0.4%
698.8 2
 
0.4%
1269 2
 
0.4%
Other values (534) 549
96.5%
ValueCountFrequency (%)
185.2 1
0.2%
223.6 1
0.2%
240.1 1
0.2%
242.2 1
0.2%
248 1
0.2%
249.8 1
0.2%
259.2 1
0.2%
268.6 1
0.2%
270 1
0.2%
273.9 1
0.2%
ValueCountFrequency (%)
4254 1
0.2%
3432 1
0.2%
3234 1
0.2%
3216 1
0.2%
3143 1
0.2%
2944 1
0.2%
2906 1
0.2%
2782 1
0.2%
2642 1
0.2%
2615 1
0.2%

smoothness_worst
Real number (ℝ)

HIGH CORRELATION 

Distinct411
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13236859
Minimum0.07117
Maximum0.2226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-07-19T12:30:57.886457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.07117
5-th percentile0.095734
Q10.1166
median0.1313
Q30.146
95-th percentile0.17184
Maximum0.2226
Range0.15143
Interquartile range (IQR)0.0294

Descriptive statistics

Standard deviation0.022832429
Coefficient of variation (CV)0.17249129
Kurtosis0.51782519
Mean0.13236859
Median Absolute Deviation (MAD)0.0147
Skewness0.415426
Sum75.31773
Variance0.00052131983
MonotonicityNot monotonic
2023-07-19T12:30:58.312812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1347 4
 
0.7%
0.1275 4
 
0.7%
0.1223 4
 
0.7%
0.1401 4
 
0.7%
0.1234 4
 
0.7%
0.1415 4
 
0.7%
0.1256 4
 
0.7%
0.1312 4
 
0.7%
0.1216 4
 
0.7%
0.1426 3
 
0.5%
Other values (401) 530
93.1%
ValueCountFrequency (%)
0.07117 1
0.2%
0.08125 1
0.2%
0.08409 1
0.2%
0.08484 1
0.2%
0.08567 1
0.2%
0.08774 1
0.2%
0.08799 1
0.2%
0.08822 1
0.2%
0.08864 1
0.2%
0.08949 1
0.2%
ValueCountFrequency (%)
0.2226 1
0.2%
0.2184 1
0.2%
0.2098 1
0.2%
0.2006 1
0.2%
0.1909 1
0.2%
0.1902 1
0.2%
0.1883 1
0.2%
0.1878 1
0.2%
0.1873 1
0.2%
0.1862 1
0.2%

Interactions

2023-07-19T12:30:48.732791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:31.835073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:34.834507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:37.184035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:40.611498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:43.727710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:46.426932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:49.080647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:32.496174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:35.139428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:37.603850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:41.094387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:44.070392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:46.732956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:49.550929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:32.985194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:35.430313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:38.811224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:41.441286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:44.361309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:47.069892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:50.084316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:33.370364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:35.756830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:39.150549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:41.823424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:44.696126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:47.396823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:50.439622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:33.695513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:36.073167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:39.489607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:42.250063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:45.085064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:47.705185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:50.829465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:34.021329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:36.413298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:39.931458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:42.764395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:45.503033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:48.003745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:51.209536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:34.509891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:36.762018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:40.264982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:43.254301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:46.060477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-19T12:30:48.412072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-19T12:30:58.593174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
concave points_meanradius_worstperimeter_worstconcave points_worsttexture_meanarea_worstsmoothness_worstdiagnosis
concave points_mean1.0000.7870.8140.9370.3070.7800.4900.820
radius_worst0.7871.0000.9940.7810.3670.9990.2190.825
perimeter_worst0.8140.9941.0000.8130.3750.9920.2410.836
concave points_worst0.9370.7810.8131.0000.3190.7740.5440.845
texture_mean0.3070.3670.3750.3191.0000.3680.1010.468
area_worst0.7800.9990.9920.7740.3681.0000.2100.792
smoothness_worst0.4900.2190.2410.5440.1010.2101.0000.420
diagnosis0.8200.8250.8360.8450.4680.7920.4201.000

Missing values

2023-07-19T12:30:51.640018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-19T12:30:52.056134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

diagnosisconcave points_meanradius_worstperimeter_worstconcave points_worsttexture_meanarea_worstsmoothness_worst
010.1471025.38184.600.265410.382019.00.1622
110.0701724.99158.800.186017.771956.00.1238
210.1279023.57152.500.243021.251709.00.1444
310.1052014.9198.870.257520.38567.70.2098
410.1043022.54152.200.162514.341575.00.1374
510.0808915.47103.400.174115.70741.60.1791
610.0740022.88153.200.193219.981606.00.1442
710.0598517.06110.600.155620.83897.00.1654
810.0935315.49106.200.206021.82739.30.1703
910.0854315.0997.650.221024.04711.40.1853
diagnosisconcave points_meanradius_worstperimeter_worstconcave points_worsttexture_meanarea_worstsmoothness_worst
55900.0410512.48082.280.0965323.93474.20.12980
56000.0430415.300100.200.1048027.15706.70.12410
56100.0000011.92075.190.0000029.37439.60.09267
56210.0942917.520128.700.2356030.62915.00.14170
56310.1474024.290179.100.2542025.091819.00.14070
56410.1389025.450166.100.2216022.392027.00.14100
56510.0979123.690155.000.1628028.251731.00.11660
56610.0530218.980126.700.1418028.081124.00.11390
56710.1520025.740184.600.2650029.331821.00.16500
56800.000009.45659.160.0000024.54268.60.08996